ArticleScientific reports2025
Prediction and design of thermostable proteins with a desired melting temperature.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Genome-Resolved Metagenomics Reveals Thermophilic Microbial Diversity and Putative Hydrolase-Encoding Genes in the El Tatio Geothermal Field.International journal of molecular sciences · 2026Article
- Molecular Adaptations of Thermophilic Proteins-From Mechanistic Understanding to Machine Learning Approaches.ChemistryOpen · 2026Review
- Dynamics-informed multigraph neural networks for protein thermostability prediction and residue-level interpretation.iScience · 2026Article
- An Escherichia coli strain with recombinant Aspergillus spp. pectinase activity for application in bioscouring.Journal, genetic engineering & biotechnology · 2025Article
- Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach.Toxins · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The stability of proteins at higher temperatures is crucial for their functionality, which is measured by their melting temperature (Tm). The Tm is the temperature at which 50% of the protein loses its native structure and activity. Existing methods for predicting Tm have two major limitations: first, they are often trained on redundant proteins, and second, they do not allow users to design proteins with the desired Tm. To address these limitations, we developed a regression method for predicting the Tm value of proteins using 17,312 non-redundant proteins, where no two proteins are more than 40% similar. We used 80% of the data for training and testing and the remaining 20% for validation. Initially, we developed a machine learning model using standard features from protein sequences. Our best model, developed using Shannon entropy for all residues, achieved the highest Pearson correlation of 0.80 with an R
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